A landscape scale study in Southeast China investigating the effects of varied green space types on atmospheric PM2.5 in mid-winter

《A landscape scale study in Southeast China investigating the effects of varied green space types on atmospheric PM2.5 in mid-winter》

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作者: Longyan Cai;Mazhan Zhuang;Yin Ren

来源: URBAN FORESTRY & URBAN GREENING,Vol.49,Issue1,Article 126607

语言: 英文

关键字: Air pollution;Forestland;Grassland;Interactive effect;Landscape pattern;Scale effect

作者单位: Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China;Xiamen Institute of Environmental Science, Xiamen, CN, 361006, China;Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China;Xiamen Institute of Environmental Science, Xiamen, CN, 361006, China

摘要: It has been established that green space is capable of reducing atmospheric PM2.5 concentrations. However, the differing effects of landscape patterns from various types of green spaces on PM2.5 are still not well understood. In addition, little information is available on individual and interactive effects of different green spaces and environmental factors on PM2.5. In this study, we analyzed the relationships between different green spaces, environmental factors, and PM2.5 in mid-winter (January) in southeastern China using redundancy analysis (RDA) and simple correlation analyses at landscape scales. PM2.5 distribution was dominated by meteorological factors (an average of 46.21 %), and green space showed immense potential for PM2.5 reduction, as indicated by strong interaction between green space–non green space on PM2.5 (33.66 % at the 5000 scale). The individual and interactive effects of forestland on PM2.5 were, on average, 1.6 times higher than those of grassland. But obvious interaction between grassland–non grassland on PM2.5 was also observed at large scales (19.16 % at the 5000 scale). This suggests that grassland has the ability to reduce atmospheric PM2.5 concentrations. Unfortunately, the effect of grassland on PM2.5 was overlooked among previous small–scale studies owing to neglecting the scale effect. In reality, the effects of different green spaces on PM2.5 are more accurately assessed at broader scales. Additionally, significant negative correlation existed between green space area and PM2.5, indicating that more green space can directly absorb and adsorb more PM2.5. The results demonstrate that planting more trees and grasses to improve urban air conditions is beneficial, effective, and recommended. Given the cost and benefit analysis, planting grasses is a more affordable option for PM2.5 reduction, particularly at the 1000–3000 m scales. Moreover, correlation analyses further indicated that as green space and forestland landscape fragmentation increased and aggregation was reduced, PM2.5 concentrations increased. Our results provide useful input for green space planning in this area.